Data exploration

#reading data from file 
import pandas as pd
df=pd.read_csv("ADANIPORTS.csv")
df.head()

	Date	Symbol	Series	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
0	2007-11-27	MUNDRAPORT	EQ	440.00	770.00	1050.00	770.0	959.0	962.90	984.72	27294366	2.687719e+15	NaN	9859619	0.3612
1	2007-11-28	MUNDRAPORT	EQ	962.90	984.00	990.00	874.0	885.0	893.90	941.38	4581338	4.312765e+14	NaN	1453278	0.3172
2	2007-11-29	MUNDRAPORT	EQ	893.90	909.00	914.75	841.0	887.0	884.20	888.09	5124121	4.550658e+14	NaN	1069678	0.2088
3	2007-11-30	MUNDRAPORT	EQ	884.20	890.00	958.00	890.0	929.0	921.55	929.17	4609762	4.283257e+14	NaN	1260913	0.2735
4	2007-12-03	MUNDRAPORT	EQ	921.55	939.75	995.00	922.0	980.0	969.30	965.65	2977470	2.875200e+14	NaN	816123	0.2741

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3322 entries, 0 to 3321
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Date                3322 non-null   object 
 1   Symbol              3322 non-null   object 
 2   Series              3322 non-null   object 
 3   Prev Close          3322 non-null   float64
 4   Open                3322 non-null   float64
 5   High                3322 non-null   float64
 6   Low                 3322 non-null   float64
 7   Last                3322 non-null   float64
 8   Close               3322 non-null   float64
 9   VWAP                3322 non-null   float64
 10  Volume              3322 non-null   int64  
 11  Turnover            3322 non-null   float64
 12  Trades              2456 non-null   float64
 13  Deliverable Volume  3322 non-null   int64  
 14  %Deliverble         3322 non-null   float64
dtypes: float64(10), int64(2), object(3)
memory usage: 389.4+ KB

# converting data types of data
df['Date']=pd.to_datetime(df['Date'])

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3322 entries, 0 to 3321
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   Date                3322 non-null   datetime64[ns]
 1   Symbol              3322 non-null   object        
 2   Series              3322 non-null   object        
 3   Prev Close          3322 non-null   float64       
 4   Open                3322 non-null   float64       
 5   High                3322 non-null   float64       
 6   Low                 3322 non-null   float64       
 7   Last                3322 non-null   float64       
 8   Close               3322 non-null   float64       
 9   VWAP                3322 non-null   float64       
 10  Volume              3322 non-null   int64         
 11  Turnover            3322 non-null   float64       
 12  Trades              2456 non-null   float64       
 13  Deliverable Volume  3322 non-null   int64         
 14  %Deliverble         3322 non-null   float64       
dtypes: datetime64[ns](1), float64(10), int64(2), object(2)
memory usage: 389.4+ KB

Feature enginnering

#finding null values from data
df.isnull().sum()

Date                    0
Symbol                  0
Series                  0
Prev Close              0
Open                    0
High                    0
Low                     0
Last                    0
Close                   0
VWAP                    0
Volume                  0
Turnover                0
Trades                866
Deliverable Volume      0
%Deliverble             0
dtype: int64

val=df['Trades'].mean()
val

44922.58835504886

# we have removes the null values by using mean because these are first 866 values.
df['Trades']=df['Trades'].fillna(val)

df['Trades'].isnull().sum()

0

# Describing data and finding it's quartiles 
df.describe()

	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
count	3322.000000	3322.000000	3322.000000	3322.000000	3322.000000	3322.000000	3322.000000	3.322000e+03	3.322000e+03	3.322000e+03	3.322000e+03	3322.000000
mean	344.114314	344.763019	351.608007	337.531969	344.239539	344.201626	344.853182	2.954564e+06	1.070144e+14	4.492259e+04	1.207441e+06	0.445899
std	192.936882	193.619992	198.617808	188.676614	193.187813	193.045886	193.841305	4.104227e+06	2.625564e+14	4.318821e+04	1.398640e+06	0.160496
min	108.000000	108.000000	110.450000	105.650000	108.000000	108.000000	108.340000	1.236600e+04	2.415857e+11	3.660000e+02	5.383000e+03	0.067000
25%	164.312500	164.850000	168.000000	161.600000	164.075000	164.312500	164.855000	7.493682e+05	1.817650e+13	2.643650e+04	3.212005e+05	0.332900
50%	324.700000	325.750000	331.275000	319.850000	325.000000	324.700000	325.765000	2.007292e+06	5.836041e+13	4.492259e+04	8.132775e+05	0.445650
75%	400.912500	401.000000	407.187500	395.000000	400.912500	400.912500	400.607500	3.636883e+06	1.158526e+14	4.585475e+04	1.605528e+06	0.555850
max	1307.450000	1310.250000	1324.000000	1270.000000	1308.000000	1307.450000	1302.150000	9.771788e+07	8.160988e+15	1.205984e+06	2.241652e+07	0.979800

import plotly.express as px
#getting more clear with data and trends
fig = px.scatter_matrix(df, dimensions=["Open", "High", "Low","Last","Close","VWAP"], color="Symbol")
fig.show()

#identify the counts of symbols 
fig = px.parallel_categories(df)
fig.show()

from sklearn.preprocessing import LabelEncoder
categorical_features = ['Symbol', 'Series']
# Create a LabelEncoder object for each categorical variable
label_encoders = {}
for categorical_feature in categorical_features:
    label_encoders[categorical_feature] = LabelEncoder()

# Encode the categorical variables using the LabelEncoder objects
for categorical_feature in categorical_features:
    df[categorical_feature] = label_encoders[categorical_feature].fit_transform(df[categorical_feature])

df.head()

	Date	Symbol	Series	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
0	2007-11-27	1	0	440.00	770.00	1050.00	770.0	959.0	962.90	984.72	27294366	2.687719e+15	44922.588355	9859619	0.3612
1	2007-11-28	1	0	962.90	984.00	990.00	874.0	885.0	893.90	941.38	4581338	4.312765e+14	44922.588355	1453278	0.3172
2	2007-11-29	1	0	893.90	909.00	914.75	841.0	887.0	884.20	888.09	5124121	4.550658e+14	44922.588355	1069678	0.2088
3	2007-11-30	1	0	884.20	890.00	958.00	890.0	929.0	921.55	929.17	4609762	4.283257e+14	44922.588355	1260913	0.2735
4	2007-12-03	1	0	921.55	939.75	995.00	922.0	980.0	969.30	965.65	2977470	2.875200e+14	44922.588355	816123	0.2741

from sklearn.preprocessing import MinMaxScaler
# Identify the numerical features in the stock DataFrame
numerical_features = ['Prev Close','Open', 'High', 'Low','Last','Close', 'Volume','VWAP','Turnover','Trades']
# Create a MinMaxScaler object
scaler = MinMaxScaler()
# Fit the scaler to the numerical features
scaler.fit(df[numerical_features])
# Transform the numerical features using the fitted scaler
normalized_features = scaler.transform(df[numerical_features])
# Update the stock DataFrame with the normalized features
df[numerical_features] = normalized_features
# Save the preprocessed stock DataFrame
df.to_csv('preprocessed_stock_dataset.csv', index=False)

from sklearn.feature_selection import SelectKBest, f_classif
stocks_df=pd.read_csv("preprocessed_stock_dataset.csv")
stocks_df.head()

	Date	Symbol	Series	Prev Close	Open	High	Low	Last	Close	VWAP	Volume	Turnover	Trades	Deliverable Volume	%Deliverble
0	2007-11-27	1	0	0.276794	0.550634	0.774216	0.570576	0.709167	0.712743	0.734103	0.279227	0.329318	0.036957	9859619	0.3612
1	2007-11-28	1	0	0.712743	0.728634	0.724774	0.659896	0.647500	0.655217	0.697799	0.046763	0.052818	0.036957	1453278	0.3172
2	2007-11-29	1	0	0.655217	0.666251	0.662766	0.631554	0.649167	0.647130	0.653161	0.052318	0.055733	0.036957	1069678	0.2088
3	2007-11-30	1	0	0.647130	0.650447	0.698406	0.673638	0.684167	0.678269	0.687572	0.047054	0.052456	0.036957	1260913	0.2735
4	2007-12-03	1	0	0.678269	0.691828	0.728895	0.701121	0.726667	0.718079	0.718129	0.030347	0.035202	0.036957	816123	0.2741

dfr = df[['Close','Open','High','Low','Last','Prev Close']]
cor_matrix = dfr.corr()
cor_matrix

	Close	Open	High	Low	Last	Prev Close
Close	1.000000	0.997934	0.999058	0.998904	0.999961	0.995193
Open	0.997934	1.000000	0.998697	0.998741	0.997855	0.997228
High	0.999058	0.998697	1.000000	0.997991	0.999045	0.995592
Low	0.998904	0.998741	0.997991	1.000000	0.998788	0.996221
Last	0.999961	0.997855	0.999045	0.998788	1.000000	0.995130
Prev Close	0.995193	0.997228	0.995592	0.996221	0.995130	1.000000

import seaborn as sns
import matplotlib.pyplot as plt
#From this correlation analysis we can find that the every independent variable is having
#a high correlatiion with the dependent variable.
sns.heatmap(cor_matrix , cmap = 'coolwarm' , annot = False)
plt.title('Correlation of Variables ')

Text(0.5, 1.0, 'Correlation of Variables ')

#Checking the relationship of each variable with Dependent variable 
sns.regplot(x = df[['Close']], y = df[['Open']], data= df)
plt.title("Relationship of Closing and Opening price")

Text(0.5, 1.0, 'Relationship of Closing and Opening price')

#Relationship of Closing and High price
sns.regplot(x = df[['Close']], y = df[['High']], data= df)
plt.title("Relationship of Closing and High price")

Text(0.5, 1.0, 'Relationship of Closing and High price')

sns.regplot(x = df[['Close']], y = df[['Low']], data= df)
plt.title("Relationship of Closing and Low price")

Text(0.5, 1.0, 'Relationship of Closing and Low price')

sns.regplot(x = df[['Close']], y = df[['Last']], data= df)
plt.title("Relationship of Closing and Last price")

Text(0.5, 1.0, 'Relationship of Closing and Last price')

sns.regplot(x = df[['Close']], y = df[['Prev Close']], data= df)
plt.title("Relationship of Closing and Previouse close price")

Text(0.5, 1.0, 'Relationship of Closing and Previouse close price')

#Alloting the Dependent and Independent Variable
y = df[['Close']]
x = df[['Open','High','Low','Last','Prev Close']]

#Spiliting the Dependent and Independet Variables in Training and Testings values
from sklearn.model_selection import train_test_split 
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3 , random_state  = 0)

#dimentions of training models
x_train.shape

(2325, 5)

#dimentions of testing models
x_test.shape

(997, 5)

from sklearn.linear_model import LinearRegression
# training model
lm = LinearRegression()
lm.fit(x_train,y_train)

print('Intercept: ' ,  lm.intercept_)
print('coefficient : ' ,  lm.coef_)

Intercept:  [8.36016716e-05]
coefficient :  [[-0.06094679  0.07256409  0.12031454  0.87010004 -0.00417572]]

#Predicting the model 
yhat = lm.predict(x_test)

print(yhat[:10])

[[0.00631646]
 [0.03618599]
 [0.19698612]
 [0.1123224 ]
 [0.26726579]
 [0.0388566 ]
 [0.64792116]
 [0.20915232]
 [0.13642555]
 [0.03575146]]

print({'Actual': y_test, 'Predicted': yhat})

{'Actual':          Close
1182  0.007170
1396  0.036558
1773  0.197716
2057  0.111676
2520  0.267498
...        ...
2300  0.168661
1379  0.035308
2100  0.062404
746   0.031431
1917  0.188920

[997 rows x 1 columns], 'Predicted': array([[0.00631646],
       [0.03618599],
       [0.19698612],
       [0.1123224 ],
       [0.26726579],
       [0.0388566 ],
       [0.64792116],
       [0.20915232],
       [0.13642555],
       [0.03575146],
       [0.37425895],
       [0.20865564],
       [0.16150065],
       [0.39065828],
       [0.14656693],
       [0.13198931],
       [0.17027852],
       [0.96885307],
       [0.04063208],
       [0.12415679],
       [0.4953962 ],
       [0.13123135],
       [0.21967915],
       [0.52919871],
       [0.20524422],
       [0.53460245],
       [0.15520969],
       [0.14887643],
       [0.03843309],
       [0.17968596],
       [0.06423629],
       [0.36534278],
       [0.22687678],
       [0.38810698],
       [0.79146577],
       [0.3113638 ],
       [0.09609237],
       [0.15514092],
       [0.12226543],
       [0.03193848],
       [0.19834181],
       [0.23657218],
       [0.21702111],
       [0.01265987],
       [0.15370139],
       [0.23154348],
       [0.25197681],
       [0.37565874],
       [0.1783243 ],
       [0.01895453],
       [0.35036133],
       [0.02758339],
       [0.1991408 ],
       [0.02474008],
       [0.51871177],
       [0.56928375],
       [0.17451735],
       [0.0117702 ],
       [0.16090162],
       [0.21816103],
       [0.04765847],
       [0.1683212 ],
       [0.17758836],
       [0.41719872],
       [0.01863105],
       [0.00565635],
       [0.03016971],
       [0.19415862],
       [0.33599861],
       [0.20662479],
       [0.24013376],
       [0.5960684 ],
       [0.20963561],
       [0.10095272],
       [0.24107905],
       [0.21390937],
       [0.22384399],
       [0.03344625],
       [0.19030603],
       [0.22675633],
       [0.03837125],
       [0.16969676],
       [0.14357878],
       [0.1307556 ],
       [0.19949509],
       [0.15177525],
       [0.52478257],
       [0.03568332],
       [0.08281341],
       [0.29042668],
       [0.24917455],
       [0.1538589 ],
       [0.02923768],
       [0.03945552],
       [0.17086518],
       [0.17767154],
       [0.0201706 ],
       [0.35962669],
       [0.1492096 ],
       [0.24070797],
       [0.08177031],
       [0.02426273],
       [0.17004626],
       [0.568165  ],
       [0.180839  ],
       [0.03526899],
       [0.45237956],
       [0.52311531],
       [0.42228286],
       [0.65950142],
       [0.03040722],
       [0.19397777],
       [0.50544763],
       [0.25643237],
       [0.24730778],
       [0.1964224 ],
       [0.44257502],
       [0.1383764 ],
       [0.26284768],
       [0.33487687],
       [0.60647319],
       [0.57541777],
       [0.21021293],
       [0.21528407],
       [0.38980696],
       [0.21824086],
       [0.00313696],
       [0.01535143],
       [0.3750666 ],
       [0.16526596],
       [0.12900725],
       [0.21249343],
       [0.2477313 ],
       [0.78268254],
       [0.13431252],
       [0.04341983],
       [0.04771664],
       [0.25681342],
       [0.03236772],
       [0.19556386],
       [0.13645743],
       [0.03821074],
       [0.49598491],
       [0.38720068],
       [0.44144143],
       [0.35475718],
       [0.17547383],
       [0.23077774],
       [0.01852798],
       [0.04653258],
       [0.12205291],
       [0.24885978],
       [0.22740621],
       [0.25640822],
       [0.02228056],
       [0.23013611],
       [0.13283088],
       [0.02898219],
       [0.19276384],
       [0.22549146],
       [0.52869714],
       [0.21410412],
       [0.51707311],
       [0.04366455],
       [0.15132975],
       [0.27438502],
       [0.27370241],
       [0.0267687 ],
       [0.23469743],
       [0.21663428],
       [0.02135113],
       [0.24225491],
       [0.17836109],
       [0.03848665],
       [0.10145266],
       [0.01677094],
       [0.1676515 ],
       [0.24307194],
       [0.39128245],
       [0.1752277 ],
       [0.15654077],
       [0.34973253],
       [0.02425267],
       [0.140317  ],
       [0.81316745],
       [0.08283851],
       [0.21644378],
       [0.02211633],
       [0.22037742],
       [0.22833886],
       [0.19392128],
       [0.03312693],
       [0.0484279 ],
       [0.13534528],
       [0.40970907],
       [0.16720409],
       [0.12101175],
       [0.05836124],
       [0.03487509],
       [0.23566549],
       [0.23457717],
       [0.03545319],
       [0.03237176],
       [0.10452347],
       [0.19777776],
       [0.23269168],
       [0.03880861],
       [0.17820407],
       [0.2392903 ],
       [0.2173824 ],
       [0.49642385],
       [0.16888095],
       [0.0322742 ],
       [0.2349573 ],
       [0.22288568],
       [0.36751284],
       [0.16762526],
       [0.24968048],
       [0.04449034],
       [0.26098143],
       [0.19977794],
       [0.48150292],
       [0.53403205],
       [0.20019838],
       [0.21948555],
       [0.13249368],
       [0.20777699],
       [0.01333307],
       [0.13351462],
       [0.33582526],
       [0.03524104],
       [0.18028708],
       [0.01277167],
       [0.00917215],
       [0.04108976],
       [0.21230934],
       [0.0743793 ],
       [0.13695754],
       [0.03832485],
       [0.00380495],
       [0.04134986],
       [0.01533353],
       [0.42481111],
       [0.84300653],
       [0.20740094],
       [0.35672342],
       [0.06346949],
       [0.22780904],
       [0.06043245],
       [0.03052817],
       [0.18767611],
       [0.53109524],
       [0.10076164],
       [0.03637362],
       [0.23548523],
       [0.23123542],
       [0.27199893],
       [0.12841862],
       [0.03673378],
       [0.13467821],
       [0.22584213],
       [0.57236236],
       [0.16018587],
       [0.04984412],
       [0.18474058],
       [0.17841049],
       [0.34447466],
       [0.58689815],
       [0.17053224],
       [0.23307498],
       [0.0157414 ],
       [0.17235784],
       [0.54034996],
       [0.13345953],
       [0.17214542],
       [0.27407989],
       [0.01389338],
       [0.41347645],
       [0.57687609],
       [0.26273956],
       [0.11672372],
       [0.10967751],
       [0.16849722],
       [0.1938245 ],
       [0.19478532],
       [0.13703438],
       [0.10239839],
       [0.01666063],
       [0.04354209],
       [0.36463355],
       [0.03387371],
       [0.03678561],
       [0.04356809],
       [0.58791196],
       [0.03553374],
       [0.12815627],
       [0.2979276 ],
       [0.19582141],
       [0.12870068],
       [0.01830071],
       [0.02292191],
       [0.22808684],
       [0.17456418],
       [0.48188798],
       [0.24378583],
       [0.31563179],
       [0.40141161],
       [0.03289973],
       [0.56504378],
       [0.03437274],
       [0.19306084],
       [0.16276887],
       [0.01051347],
       [0.013521  ],
       [0.02883222],
       [0.54929628],
       [0.53692565],
       [0.3966008 ],
       [0.53765013],
       [0.01442943],
       [0.18019162],
       [0.1032852 ],
       [0.20504873],
       [0.2214555 ],
       [0.03900341],
       [0.32002781],
       [0.0357151 ],
       [0.08009523],
       [0.40159807],
       [0.57235462],
       [0.53933825],
       [0.21243895],
       [0.10352348],
       [0.14830547],
       [0.37004928],
       [0.03868456],
       [0.17302852],
       [0.03429542],
       [0.21580065],
       [0.45691889],
       [0.22578932],
       [0.50538876],
       [0.38989577],
       [0.17522302],
       [0.01350034],
       [0.18212064],
       [0.23090583],
       [0.01950843],
       [0.22753915],
       [0.25075994],
       [0.14466658],
       [0.62544832],
       [0.22032788],
       [0.55236145],
       [0.23302473],
       [0.03549564],
       [0.21815858],
       [0.03906797],
       [0.15159004],
       [0.09093585],
       [0.19429157],
       [0.04299759],
       [0.2549043 ],
       [0.26977594],
       [0.23708347],
       [0.29623998],
       [0.12809951],
       [0.21973869],
       [0.19781101],
       [0.13091528],
       [0.04060656],
       [0.17340592],
       [0.55806167],
       [0.21355512],
       [0.01413549],
       [0.34689466],
       [0.64807696],
       [0.1375856 ],
       [0.14148548],
       [0.1806938 ],
       [0.11364873],
       [0.03760013],
       [0.13164149],
       [0.22721902],
       [0.46277768],
       [0.47296099],
       [0.13519828],
       [0.23977948],
       [0.50816046],
       [0.23547144],
       [0.19999391],
       [0.37360037],
       [0.22812505],
       [0.36251632],
       [0.40321568],
       [0.04771858],
       [0.07934227],
       [0.26496283],
       [0.61216541],
       [0.03349433],
       [0.24376274],
       [0.12743493],
       [0.27442475],
       [0.23315965],
       [0.00609073],
       [0.51373086],
       [0.20269984],
       [0.06559808],
       [0.1612847 ],
       [0.23668129],
       [0.13821659],
       [0.04191234],
       [0.03938765],
       [0.2237211 ],
       [0.22961531],
       [0.01531133],
       [0.24310852],
       [0.7985166 ],
       [0.25039681],
       [0.23615388],
       [0.21257427],
       [0.18109292],
       [0.17218454],
       [0.1677702 ],
       [0.06148533],
       [0.58300859],
       [0.0291209 ],
       [0.02376124],
       [0.57594678],
       [0.21812911],
       [0.0325686 ],
       [0.04687142],
       [0.0257667 ],
       [0.1959138 ],
       [0.26967327],
       [0.16341233],
       [0.03184531],
       [0.2307826 ],
       [0.50896944],
       [0.23611554],
       [0.03684314],
       [0.13320682],
       [0.24671208],
       [0.17701277],
       [0.02377867],
       [0.13042072],
       [0.0451477 ],
       [0.1821152 ],
       [0.51915896],
       [0.02341315],
       [0.1878597 ],
       [0.18349337],
       [0.40806186],
       [0.25429005],
       [0.13485564],
       [0.2378939 ],
       [0.03285621],
       [0.38406982],
       [0.03950152],
       [0.3592562 ],
       [0.12331696],
       [0.20425208],
       [0.03290947],
       [0.04207033],
       [0.03398164],
       [0.59666244],
       [0.22377191],
       [0.03329026],
       [0.56495946],
       [0.08887974],
       [0.99870854],
       [0.27446927],
       [0.16133798],
       [0.23370095],
       [0.42712907],
       [0.12900024],
       [0.01809855],
       [0.0442792 ],
       [0.16431731],
       [0.45163874],
       [0.01982523],
       [0.08525896],
       [0.23444171],
       [0.13393699],
       [0.29041509],
       [0.01344748],
       [0.21465051],
       [0.1366045 ],
       [0.04879617],
       [0.72460595],
       [0.18517756],
       [0.4435234 ],
       [0.19800875],
       [0.24316162],
       [0.29540987],
       [0.5105466 ],
       [0.16444849],
       [0.13704244],
       [0.01247467],
       [0.00297634],
       [0.01602711],
       [0.37968103],
       [0.19831696],
       [0.19444974],
       [0.00374118],
       [0.03370688],
       [0.21842096],
       [0.22937231],
       [0.0170442 ],
       [0.01315435],
       [0.08250508],
       [0.47970534],
       [0.14133522],
       [0.01435972],
       [0.24324637],
       [0.0105298 ],
       [0.14416776],
       [0.21163192],
       [0.02404608],
       [0.21447689],
       [0.43936631],
       [0.02091156],
       [0.21946719],
       [0.03670419],
       [0.03703443],
       [0.33282378],
       [0.55229886],
       [0.16860672],
       [0.03887409],
       [0.5311642 ],
       [0.513533  ],
       [0.20909804],
       [0.44406432],
       [0.35308915],
       [0.23246113],
       [0.03725934],
       [0.51182989],
       [0.29742322],
       [0.12737621],
       [0.15127292],
       [0.21983825],
       [0.41407977],
       [0.3017699 ],
       [0.41091174],
       [0.39051635],
       [0.24169463],
       [0.04389474],
       [0.2218463 ],
       [0.17528599],
       [0.07854076],
       [0.06765321],
       [0.2321782 ],
       [0.08113161],
       [0.51613534],
       [0.47816078],
       [0.03681286],
       [0.16987703],
       [0.22541392],
       [0.04091067],
       [0.21680682],
       [0.01369309],
       [0.03256901],
       [0.17335902],
       [0.56586114],
       [0.03653242],
       [0.21198595],
       [0.21556344],
       [0.17191031],
       [0.18325772],
       [0.53313663],
       [0.03076738],
       [0.54553131],
       [0.02338367],
       [0.00654744],
       [0.42131448],
       [0.17542395],
       [0.00363866],
       [0.58204952],
       [0.02555841],
       [0.01267479],
       [0.22022023],
       [0.25386357],
       [0.50936903],
       [0.20855365],
       [0.13131398],
       [0.11535872],
       [0.230599  ],
       [0.44757459],
       [0.20239578],
       [0.23610088],
       [0.12821179],
       [0.08350508],
       [0.17152874],
       [0.03754505],
       [0.80442713],
       [0.00875663],
       [0.11128081],
       [0.10260668],
       [0.18007514],
       [0.44241518],
       [0.39659803],
       [0.89268742],
       [0.06258905],
       [0.18334563],
       [0.03969533],
       [0.13416389],
       [0.04102412],
       [0.59170917],
       [0.07085625],
       [0.19994696],
       [0.16798402],
       [0.17119298],
       [0.18593054],
       [0.18777464],
       [0.02872238],
       [0.04559681],
       [0.09838959],
       [0.24167515],
       [0.03847838],
       [0.1795245 ],
       [0.00586669],
       [0.0271578 ],
       [0.20020574],
       [0.01982102],
       [0.3122095 ],
       [0.21456528],
       [0.12460479],
       [0.45002439],
       [0.03043369],
       [0.19898985],
       [0.09091221],
       [0.02684196],
       [0.02937739],
       [0.22837606],
       [0.24783303],
       [0.22084475],
       [0.19614924],
       [0.12476349],
       [0.18502265],
       [0.02699796],
       [0.11810401],
       [0.2161299 ],
       [0.05259768],
       [0.01983078],
       [0.43747678],
       [0.08496842],
       [0.01623855],
       [0.35913259],
       [0.18878917],
       [0.0057946 ],
       [0.21578385],
       [0.40876015],
       [0.1394717 ],
       [0.19089375],
       [0.15052814],
       [0.22842144],
       [0.02546109],
       [0.0291328 ],
       [0.02727003],
       [0.06029477],
       [0.10018619],
       [0.23621096],
       [0.251608  ],
       [0.04561889],
       [0.00184244],
       [0.15460398],
       [0.19341217],
       [0.03491596],
       [0.48214823],
       [0.03718351],
       [0.11744312],
       [0.01377249],
       [0.1732623 ],
       [0.09409246],
       [0.00230192],
       [0.17906561],
       [0.1852343 ],
       [0.23126047],
       [0.02916685],
       [0.14290588],
       [0.28578954],
       [0.13163934],
       [0.07072955],
       [0.02095796],
       [0.22579283],
       [0.21355401],
       [0.19619337],
       [0.14132377],
       [0.36693162],
       [0.03289883],
       [0.17275435],
       [0.03502109],
       [0.13375618],
       [0.22215834],
       [0.12870076],
       [0.50685966],
       [0.21591821],
       [0.46880471],
       [0.23233007],
       [0.2117356 ],
       [0.59330261],
       [0.01131728],
       [0.1308581 ],
       [0.57435025],
       [0.33481412],
       [0.03125941],
       [0.24566852],
       [0.22694051],
       [0.21020375],
       [0.18715983],
       [0.07127985],
       [0.03069401],
       [0.16922075],
       [0.04623274],
       [0.02244441],
       [0.03773651],
       [0.02403582],
       [0.28775985],
       [0.04448287],
       [0.23023144],
       [0.34812835],
       [0.26769376],
       [0.33337163],
       [0.09012088],
       [0.09638412],
       [0.05215035],
       [0.03474677],
       [0.02406135],
       [0.14040109],
       [0.37830174],
       [0.03506289],
       [0.0664409 ],
       [0.18971212],
       [0.51537484],
       [0.22957602],
       [0.01257795],
       [0.18081393],
       [0.20898044],
       [0.13768934],
       [0.53192162],
       [0.40041853],
       [0.2176546 ],
       [0.22279197],
       [0.39823699],
       [0.17553118],
       [0.01307661],
       [0.08631183],
       [0.10738628],
       [0.12725135],
       [0.01984216],
       [0.39482659],
       [0.3743    ],
       [0.11997875],
       [0.24792046],
       [0.03226214],
       [0.18253588],
       [0.25649122],
       [0.53436927],
       [0.17459944],
       [0.04297405],
       [0.18473021],
       [0.00492713],
       [0.07746523],
       [0.20053243],
       [0.21039762],
       [0.20930788],
       [0.03212312],
       [0.6054445 ],
       [0.20615148],
       [0.48281787],
       [0.05953865],
       [0.02781694],
       [0.22499037],
       [0.04258294],
       [0.2106083 ],
       [0.19913291],
       [0.23399008],
       [0.3864704 ],
       [0.20009956],
       [0.0273974 ],
       [0.0197919 ],
       [0.56816256],
       [0.23767455],
       [0.01201855],
       [0.02533894],
       [0.18124776],
       [0.36694024],
       [0.00655603],
       [0.18696929],
       [0.01432035],
       [0.37034052],
       [0.52925714],
       [0.01818443],
       [0.35321013],
       [0.0439094 ],
       [0.17560812],
       [0.25337828],
       [0.24213057],
       [0.78662873],
       [0.02657215],
       [0.00996607],
       [0.22201852],
       [0.22217741],
       [0.25598304],
       [0.23677759],
       [0.02977411],
       [0.02716604],
       [0.1739243 ],
       [0.0269396 ],
       [0.13530912],
       [0.03245438],
       [0.30187538],
       [0.02664737],
       [0.40888013],
       [0.26899274],
       [0.22217967],
       [0.01321624],
       [0.16572694],
       [0.21872993],
       [0.02516097],
       [0.35841849],
       [0.51869703],
       [0.37153566],
       [0.11860613],
       [0.17656327],
       [0.16697356],
       [0.25264557],
       [0.0161542 ],
       [0.23267023],
       [0.22755523],
       [0.1513894 ],
       [0.38665251],
       [0.18534293],
       [0.24732085],
       [0.21122728],
       [0.03250462],
       [0.20517808],
       [0.1398048 ],
       [0.36379117],
       [0.03248819],
       [0.27254076],
       [0.50711643],
       [0.07872371],
       [0.51059429],
       [0.18627006],
       [0.40179374],
       [0.63163711],
       [0.24043508],
       [0.02591459],
       [0.24848348],
       [0.03715249],
       [0.15995102],
       [0.51538303],
       [0.48775739],
       [0.27553127],
       [0.01344654],
       [0.02371379],
       [0.01034969],
       [0.18995784],
       [0.14023307],
       [0.39646383],
       [0.0413945 ],
       [0.15185393],
       [0.13721937],
       [0.18756077],
       [0.2175824 ],
       [0.21562716],
       [0.01034951],
       [0.02288165],
       [0.08813082],
       [0.42824886],
       [0.02828318],
       [0.53241164],
       [0.21146482],
       [0.01853347],
       [0.01209696],
       [0.02063168],
       [0.19589004],
       [0.03016359],
       [0.3356222 ],
       [0.36130752],
       [0.02044048],
       [0.01937215],
       [0.02904321],
       [0.03536371],
       [0.34424877],
       [0.0715901 ],
       [0.15166507],
       [0.02109129],
       [0.25217189],
       [0.02820744],
       [0.18215351],
       [0.0245648 ],
       [0.15259594],
       [0.23566465],
       [0.24940612],
       [0.43295321],
       [0.1264385 ],
       [0.03770872],
       [0.03893708],
       [0.03689801],
       [0.22507832],
       [0.01940638],
       [0.5886273 ],
       [0.24782423],
       [0.51433644],
       [0.20930968],
       [0.07945867],
       [0.03417024],
       [0.02070685],
       [0.56197505],
       [0.01450307],
       [0.20539653],
       [0.02163001],
       [0.04985627],
       [0.25754531],
       [0.21958295],
       [0.17233183],
       [0.51020806],
       [0.21554184],
       [0.14360092],
       [0.22300042],
       [0.03147814],
       [0.04755838],
       [0.01589131],
       [0.62042699],
       [0.14875783],
       [0.08012583],
       [0.0430636 ],
       [0.15604499],
       [0.01663859],
       [0.24776009],
       [0.01817706],
       [0.15739394],
       [0.30083571],
       [0.19551225],
       [0.2195802 ],
       [0.01552356],
       [0.02713362],
       [0.22888161],
       [0.25393342],
       [0.01677282],
       [0.03877754],
       [0.02998498],
       [0.19619915],
       [0.02146127],
       [0.19332286],
       [0.52059046],
       [0.32874806],
       [0.93897335],
       [0.20323023],
       [0.03348429],
       [0.11894916],
       [0.33983201],
       [0.36133244],
       [0.03668596],
       [0.23023587],
       [0.00333818],
       [0.17428017],
       [0.03145703],
       [0.19890644],
       [0.20976383],
       [0.23968301],
       [0.21829186],
       [0.46509558],
       [0.01518898],
       [0.01751971],
       [0.13259039],
       [0.03510039],
       [0.12652641],
       [0.04544215],
       [0.13023667],
       [0.41704855],
       [0.21623784],
       [0.17263613],
       [0.50426849],
       [0.12826793],
       [0.30687446],
       [0.02276549],
       [0.24878343],
       [0.04656642],
       [0.39076027],
       [0.25100204],
       [0.21685007],
       [0.02672718],
       [0.21340187],
       [0.21555318],
       [0.03947591],
       [0.01254886],
       [0.22046853],
       [0.20555926],
       [0.04359588],
       [0.16896081],
       [0.03548178],
       [0.06260621],
       [0.03138168],
       [0.18860133]])}

axl = sns.distplot(y_test, hist = False , color  = 'r')

sns.distplot(yhat, hist = False , color = 'b', ax = axl)

C:\Users\vaibh\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

C:\Users\vaibh\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).

<AxesSubplot:ylabel='Density'>

Model Evaluation

from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, yhat )

1.6117829755948168e-06

rsqr = lm.score(x,y)

print('R - Squared :' , rsqr)

R - Squared : 0.9999357269364687

From the above two model evaluation matrix we can find that the goodness of fit is 0.9999 which explians that the independent variable explians the 99.99% of the dependent variable and The average error between the predictions and actuals in this dataset is 1.6

sns.regplot( x = y_test, y = yhat , data = df)
plt.title('Relatioship of Y_test and Yhat')
plt.ylabel('y_test')
plt.xlabel('yhat')

Text(0.5, 0, 'yhat')

#if we consider the values Open : 3326 High : 3335 Low : 3302 Last : 3308 Prev Close : 3324
values = [[3326,3335,3302,3308,3324]]
closep = lm.predict(values)
print('Closing Price :', closep)

Closing Price : [[3300.98174807]]

if the Open pirce is 3326, High price is 3335 , Low price is 3302 , Last price is 3308 and Prev Close price is 3324 the closing price will be 3308.